Traditionally, in the control center of power system, the operation data of power system is acquired from SCADA (Supervisory Control And Data Acquisition) system, in which the sampling interval for remote measurements of, such as, voltage, current, power, and frequency is about 1-5 seconds and the data is without time-stamp. Thereby only the quasi-steady state of power system can be reflected. In recent years, the synchronized phasor measurement technology based on GPS has been widely used in the electric power system. PMUs based on this technique have been installed at many substations and power plants. PMUs measure voltage phasor, current phasor, power, frequency, and rotating speed of generator of each substation and power plant and transfer them with time-stamps to the control center at a sampling interval of 10ms - 30ms, thereby the control center can monitor the dynamic behaviors of the electric power system. However, at present and in the foreseeable future, only a limited number of substations or power plants are equipped with PMUs, because the corresponding cost is expensive and the PMU data transfer will require a great amount of communication bandwidth. Therefore, the existing wide area measurement system (WAMS) based on PMU cannot observe the dynamic performance of the whole power plants or substations.
In order to monitor the dynamic performance of those power plants, substations, or nodes without PMU, and to assist the operator to make on-line safe and stable decision, it is meaningful to estimate the dynamic process of the nodes without PMU in real time by use of existing on-line measurement data. In the prior arts, only a method is proposed to solve this problem. This method derives the magnitudes and the phase angles of the voltage phasors of those nodes without PMU from those of the nodes with PMU, which is based on the sensitivity relationship between the voltage phasors with PMU and those without PMU and on the corresponding SCADA measurement data. The sensitivity relationship is derived from the Jacobian matrix of power flow equations. However, the method has the following disadvantages:                (1) The elements of the original Jacobian matrix are relating to the magnitude and the phase angle of the voltage and the admittance of the nodes. When the power flow operation points or the network configurations are changed, the Jacobian matrix should be recalculated accordingly. However, the computing burden is very heavy for the recalculation of Jacobian matrix, so that it is not suitable to generate dynamic process data of 10 ms order on line. The prior arts obtain approximate coefficients of linear correlation between the nodes without PMU and those with PMU, which is achieved by ignoring the change of the voltage magnitude and phase angle, by ignoring the resistance of the elements of the grid, and by substituting the imaginary elements of the admittance matrix for the elements of the Jacobian matrix. These coefficients are only renewed when the network configuration is changed. Though this significantly reduces the workload of on-line computation, it sacrifices precision of on-line dynamic process estimation.        (2) The above simplified Jacobian matrix is obtained from the admittance matrix, and it is dependent on the admittance parameters of grid devices, such as transmission lines, transformers, and generators. Usually, these parameters are rated values provided by manufacturers or typical values. However, there often exists a big error between these values and the actual parameters of the grid devices. With the aging of devices and the changing of operation environment, the parameters may be great different from the designed one or the one measured in typical environments. The inaccuracy of parameters will affect the accuracy of the calculated coefficients of linear correlation, thus make the estimation of the measurement dynamic process of the nodes without PMU be inaccurate. The inaccuracy of the above parameters is difficult to be verified and corrected on line one by one, and hence the inaccuracy of the estimation of the dynamic process is also difficult to be corrected.        (3) Moreover, the dynamic process estimation in the prior arts is limited to voltage phasor estimation, and no any for other measurements, especially for power.        
Furthermore, it should be noted that there are many prior arts about state estimation or dynamic state estimation by using data from PMU and SCADA. However, they are to improve precision of state estimation of the current or future SCADA measurement time point by use of PMU data, none of them is to estimate the dynamic process of the nodes without PMU.
It can be concluded from the above that up to date there is no precise and practical method for estimating the dynamic process of a measurement node. In this invention, a practical dynamic process estimation method for a measurement node without PMU is proposed, which is only based on real PMU measurements and real SCADA measurement of those nodes with PMU. The method is independent of grid parameters and state matrix renewal, and doesn't need the nodes without PMU to be completely observable. The theory basis of the proposed method is linearization, which, based on the SCADA (or state estimation) data of current and historical time points, finds out the linear combination relation between the variance of measurement node without PMU and the variances of measurement nodes with PMU by using least square method. And then at PMU measurement time point, the corresponding measurement of the node without PMU is estimated from real measurements of the nodes with PMU. The whole estimation process is based on actually measured data, not depending on the parameters of grid devices and the renewal of Jacobian or state matrix, also does not need the measurement node without PMU to be completely observable. The stronger the linear combination relationship is, the smaller the error for dynamic process estimation is. For example, for the dynamic process estimation of voltage phasors, the errors of magnitude are ±0.05%, and the errors of phase angles are ±0.05°. If the linear combination relationship is weaker, the error for dynamic process estimation will become larger. For example, in the dynamic process estimation of active power during low frequency oscillation, the error usually is ±5%, but it is still in the range that can be accepted in engineering.